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ChainLink Research ‐ All Rights Reserved 2017
PreemptiveFreshness
Management
EmpoweringWorkerstoImproveDeliveredFreshness Most approaches to managing produce freshness today are forensic
and reactive. That is, they rely on after‐the‐fact forensic evidence,
typically a strip‐chart temperature recorder or other vehicle‐level
monitoring of temperature in the reefer. This evidence may help the
retailer (somewhat)1 in deciding whether to accept or reject a load,
but it does little to prevent or reduce spoilage in the chain up to that
point. Similarly, at the various process steps in the cold chain—from
harvest to pre‐cool to storage to distribution—decisions are
generally made on a reactive basis, after it becomes apparent that
things are not working to plan.
Rather than waiting until the fire starts and then frantically trying to
put it out, a preemptive approach systematically takes measures to
prevent the fire in the first place. A preemptive freshness
management system continually monitors field, pack house, DC, and
store operations, thereby knowing much earlier when there will be
capacity constraint. It then makes more intelligent decisions on
prioritization and sequence, to maximize freshness and minimize
loss. This approach results in vast improvements compared to the
on‐the‐fly human judgment calls typically made in produce
operational settings.
1 A smarter approach for retailers to evaluate freshness and make accept‐reject decisions for produce shipments is to use end‐to‐end pallet‐
level temperature monitoring.
ChainLink Research ‐ All Rights Reserved 2017
Contents
Empowering the Worker ......................................................................................................... 1
Recommending Corrective Actions Based on Current Situation and Requirements ............................... 1
Conditions and Their Impact ................................................................................................................. 1
Product Requirements .......................................................................................................................... 2
Management Preferences .................................................................................................................... 2
Resource Constraints ............................................................................................................................ 2
Process Mapping and Modeling ............................................................................................... 2
Modeling Throughput Rates ..................................................................................................................... 3
Modeling Deliverables Targets and Tradeoffs .......................................................................................... 3
Predictive Analytics ................................................................................................................. 4
Solutions Needed for Each Process Step .................................................................................. 5
Making it Easy for Front Line Workers to do the Right Thing ................................................................... 5
Preemptive Freshness Management
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EmpoweringtheWorkerWorkers in the produce cold chain—from the field, to packhouse, to distribution center (DC), to the retailer—make the best decisions they can, every day, based on the tools they have and the goals they are being measured against. This often leads to suboptimal results because the tools are rudimentary, such as just knowing the time and temperature in a pre‐cool unit, and goals are simplistic, such as ‘you can go home as soon as all these cases have been shipped.’ To empower smarter decisions, a system can be implemented that gives the worker three things:
1. Early warning of upcoming issues—The system should alert the workers, with as much advanced warning as possible, whenever impending issues are expected to prevent them from processing the produce in the proper, optimal way (i.e. it alerts workers and supervisors ahead of time if produce will be late going into pre‐cool, will not be cooled adequately, operations are going to back up and fall behind, and so forth).
2. Clear guidance on goals and tradeoffs—Helping the worker to clearly and easily understand which customers, products, and shipments have priority. Instead of handling cases in strictly FIFO2 or ‘whatever is nearest to me’ basis, the worker can take into account the priorities and tradeoffs. For example, perhaps that shipment of romaine lettuce has three more days of travel or requires more days of freshness than the iceberg lettuce shipment. Without the system telling them that, the worker has no idea. Armed with that knowledge, the worker may pre‐cool the romaine first, even though the iceberg is at the ‘front of the line’ for precooling.
3. Prescriptive corrective actions—Ideally, the system itself will be able to do a ‘what‐if’ analysis, weigh the trade‐offs, and propose the optimal preemptive or corrective actions to the worker. The system should make it easy for the workers to make the right optimal decisions and take the right actions every time, without thinking. It is impractical for a worker out in the field, at the packhouse, on the truck, or at the distribution center to stop what they are doing, run an analytics program, and then consider the tradeoffs on various choices. It is much better if they are simply instructed what to do next, even while the system is providing workers and supervisors with unambiguous clarity on what the goals are, current progress towards meeting those goals, and alerts when things are not going to plan.
Timeliness is a critical aspect of empowering smarter decisions. Workers can’t be sitting waiting for answers from the system before they can do anything … or worse yet told after‐the‐fact what went wrong, when it’s too late to correct. Alerts, guidance, and prescribed actions need to happen in near real‐time, as the work is actually happening, to effectively guide the work and avoid waste.
RecommendingCorrectiveActionsBasedonCurrentSituationandRequirementsTo make intelligent recommendations, the system needs full situational awareness and contextual knowledge.
This includes A) current conditions and their impact, B) product requirements, C) management preferences,
and D) physical or resource constraints.
ConditionsandTheirImpactExamples of the types of conditions the system needs to be aware of include the temperature at harvest time,
expected temperatures later in the day, temperature the night before, whether or not it rained recently,3 and how
many days before or after peak maturity the product is being harvested. In addition, the type of produce being
2 First‐in, First‐out 3 Rain can affect harvest throughput as it can impact access to the fields and the rate of harvest, as well as pre‐cool requirements. This effect varies with the type of produce as well.
Early Warning Alerts Example
One example of an early warning notification is the way Zest Fresh analyzes outstanding current purchase orders (POs) for a grower and quickly determines how to best allocate the current harvest against demand. Typically, a grower will slightly undercommit, but actual actions and conditions in the field and yard—such as pallets not coming in from the field as quickly as planned, or too many pallets coming in at same time—can create bottlenecks, congestion, and slowdowns during the day. These bottlenecks can erode the grower’s ability to meet their freshness commitments. As operations get congested and backed up, workers start making tradeoffs, such as processing the closest pallet first, even when other pallets have been waiting for pre‐cool longer. By analyzing the current situation against outstanding PO commitments, Zest Fresh can generate alerts and recommend corrective actions as needed.
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harvested greatly affects the impact of all these variables and other aspects of freshness. If the system does not
take into account all of these conditions and their impact on operational decisions, then it cannot prescribe the
optimal actions to workers.
ProductRequirementsAt any given time, the grower’s operations (field, precool, packhouse, shipping) has a queue of orders they are
fulfilling. Within that queue, different shipments have different requirements; in part because some buyers require
longer shelf life at delivery, while others (such as a restaurant or food processing plant) are OK with shorter
remaining shelf life. Even the same buyer may have some slow‐moving and some fast‐moving stores, where the
slow‐moving stores require longer shelf life. Another important part of the requirements equation is the transit
time from grower to customer. All these requirements need to be taken into account when prioritizing and
optimizing the steps in the operation.4
ManagementPreferencesSome customers will be given higher priority than others, perhaps because they are more strategic, have higher
volumes, larger life‐time revenue for the grower, or other considerations. In any case, the system needs to allow
the grower to enter these kinds of adjusted weightings and management preferences, including the ability to
specify rules, such as ‘Customer A is more important than Customer B, except when the order for Customer B is a
special promotion.’
ResourceConstraintsThe system must have knowledge of resource constraints in order to make the tradeoffs, and recommend actions
that are feasible and can actually be executed. Constraints include things like the capacity and rate of transport
from the field, storage space in the yard and packhouse, pre‐cool units’ capacity, number of trucks that can be
loaded at once, hours of sunlight, and so forth. This also includes fluctuating variables such as the amount of labor
available or transit time from various fields to packhouse. Things like pre‐cool unit capacity vary depending on the
type of produce, as well as external conditions, such as field temperature. Without this knowledge,
recommendations are meaningless, as they actually may not be feasible to execute in the real‐world operations.
Furthermore, the solution must be able to calculate overall capacity and throughput in order to forecast whether or
not demand can be met, and thereby generate alerts that provide alternatives, to optimize processing and
allocation in an overcommitted, under‐capacity situation.
ProcessMappingandModelingIn addition to the elements of situational awareness and context knowledge listed above (i.e. conditions,
requirements, preferences, and constraints), intelligent recommendations require a detailed and accurate
understanding of the various process steps throughout the cold chain. A set of process maps is required, modeling
the various processes across the operations and how they interconnect. For example, the process model used by
Zest Fresh factors in the temperature the night before, current temperature, expected temperature, current
backlog and expected wait time for pre‐cool (influenced by the relative priority of the cases waiting for precool). All
of these factors, and more, impact the temperature of the produce at the time it is finally placed into the pre‐cool
unit. The internal temperature of the produce, along with the type of produce, moisture, and other factors, in turn
impacts the amount of time needed to pre‐cool the produce to the optimal temperature. To optimize resource
4 Intelligent Distribution is a methodology for optimizing distribution decisions, based on the customer requirements and transit times. To learn more about Intelligent Distribution methodologies, see Pallet‐level Monitoring: Maximizing Delivered Shelf‐life in the End‐to‐End Fresh Food Supply Chain.
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planning and allocation, the solution needs a process map that incorporates all of these interrelationships and has
the needed mathematical models to factor them into its recommendations.
Figure 1 – Example Representation of a Process Map
ModelingThroughputRatesContinuing on the example above, there is a standard throughput rate assumed for the precool unit: for produce
type A,5 the unit can precool X cases per hour. But if the produce is coming in at an elevated temperature, then the
throughput of the precool unit will be reduced to something less than the standard X units per hour.6 In that case,
the system might make a recommendation to reduce the number of crews harvesting, in order to match the rate of
harvest with the capacity of the precooler. Or it might give the operations manager an alert the day before (based
on the weather forecast) and recommend starting the harvest at 4AM under lights, so that there is time to precool
the committed number of orders. Similarly, the throughput of the operation will be impacted by the distance of the
fields from the packhouse (less or more travel time per truckload, impacting both time and temperature), whether
the produce is being picked before, at, or after peak maturity (impacts days of freshness and produce size), and
many other factors.
Developing throughput models and process models that consider all of these various factors takes many years of in‐
the‐field experience. A solution that has built‐in mature process maps and models, based on depth of experience,
will give much more reliable and intelligent recommendations than one that uses an overly simplistic set of
assumptions.
ModelingDeliverablesTargetsandTradeoffsEach operation will have a set of ‘deliverables targets’—i.e. customer orders, with a specific number of cases
of produce in each order for each shift and for the whole day. This is fairly straightforward when the
5 Produce with less surface area and more core, like broccoli or cauliflower, takes longer to precool than produce with more surface area and less core, like lettuce. These standard throughput rates are generally known for each type of produce for each different model of pre‐cool unit and should be incorporated in the model. 6 Conversely, cooler than average produce will increase the throughput above the standard X cases per hour.
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operations are on a clear path to achieving their production goals for the day. It’s when you get congestion or
added constraints in the operations and you need to start making tradeoffs that things are not so simple.
When this happens (and it is quite common) the process model needs to take into account all the different
factors associated with those production targets, such as the number of days drive time to each customer’s
destination, management preferences, and differences in precool time for different types of produce, in order
to optimize the allocation of available pallets of produce.
PredictiveAnalyticsThe process maps, combined with a near real‐time view of the current situation and requirements, enables
the system to do predictive analytics. First and foremost, it means that the system can predict expected shelf
life, based on the handling and temperature exposure history of each pallet of produce. Zest Fresh also does
‘what if’ analysis, predicting the impact on shelf life of various scenarios. It is able to answer questions such as,
‘if pallets 1 through 10 sit out an extra hour, so that pallets 11 to 20 can be prioritized in the precool, can we
still meet the shelf‐life commitments and requirements for those first 10 pallets?’ It can evaluate thousands of
potential options … much more than any human could … and come up with the best combination of actions,
guided by the goals, preferences, and constraints of the operation. A grower can adjust these business rules to
meet the needs and specific priorities of their own operations. When workers are making the tradeoff
decisions, they are often suboptimal, resulting in reduced shelf life and freshness, loss of customer
satisfaction, rejection of shipments, and reduction in revenue. By letting the system find the optimal solution,
an operation can dramatically reduce waste, increase quality (freshness) consistency, and meet a much larger
percentage of customer commitments, all with the same (or reduced) amount of labor and equipment.
Figure 2 – Example Process Maps and Model Parameters for Each Process Step Across the End‐to‐End Cold Chain
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SolutionsNeededforEachProcessStepAn unbroken chain of intelligent, preemptive‐freshness processes is required, all the way from the field to the retail
shelf. If even one step is done poorly, then freshness and quality is compromised. Each process step requires its
own unique process map and model, yet they are also interconnected and interlocking, as every step is dependent
on the results of all prior process steps. An end‐to‐end process map might include models for the following
processes:
The process steps are interlinked, so the system needs to be modelled holistically, end‐to‐end. For example, if there
are unusually high temperatures one day, the system might reasonably be expected to tell workers to precool each
pallet longer, to compensate for the heat and allow the produce to reach the ideal temperature. However, if the
model does not take into consideration the rate the produce is being harvested and brought in from the fields, then
pallets could back up in the yard waiting for their slot in the precool (or worse yet, product goes unharvested and
spoils in the field). This could result in congestion, longer wait times, longer exposure to high temperatures
(therefore diminished shelf life) and so forth. Since the workers in the field are paid based on how many cases they
harvest, and anyway they don’t even see what is happening in the yard, they are not going to slow down just
because the yard is backing up. Thus, each process step is interconnected and cannot be modeled in isolation.
As another example, consider the interconnected processes in a retailer’s receiving/putaway and replenishment
operations. Each day a pallet of produce sits in the retailer’s warehouse is a day of shelf life lost. So, ideally the
retailer strives to ‘pick to zero,’ sending out to its stores all pallets of produce it receives every day. In practice, it
doesn’t always work that way and some retailers average up to three days of storage time in their DC for produce,
often due to poor visibility into actual store demand and replenishment needs, which may vary from plan.7 In these
constrained situations, the system needs to help the receiving personnel make decisions about which pallets to
cross‐dock and which to put away. A delivery of berries arriving with only four days of shelf life left should be
prioritized over a delivery of lettuce with ten days left. Other factors that may be considered in prioritizing the work
are current levels of inventory in each store, the velocity of consumption in the stores, and perhaps other
management preferences.8 The system needs to be configurable to take all of these interlocking process steps and
priorities into consideration.
MakingitEasyforFrontLineWorkerstodotheRightThingMany decisions in the produce cold chain are made every day by the workers and supervisors on the front lines: in
the field, packhouse, DC, and stores. Most of them really want to do the right thing. But workers can only be as
smart as the tools and goals (metrics) they are given. Often the primary metric of success is simple throughput, with
little or no consideration of the impact on delivered freshness (i.e. days of shelf life remaining by the time the
produce is delivered to the end customer), and other strategic goals, such as ensuring that large, long‐term, key
accounts are well served first. Furthermore, it is impossible for a worker, especially in the heat of battle when
things are backing up, to consider all of the different tradeoffs.
What is needed is a system that provides preemptive freshness management capabilities. The best we have seen is
Zest Labs’ Zest Fresh solution. No other system we’ve encountered offers the same level of preemptive freshness
management capabilities for fresh produce, providing prescriptive guidance to workers at each step of each
process, end‐to‐end, taking into account the impact across the end‐to‐end process model, in order to maximize the
strategic goals and success of the grower and retailer. This makes it easy for the front‐line workers to do the right
thing, and thereby make the overall operation more competitive, successful, and a more satisfying place to work.
7 Due to weather or other events, a store may sell out quickly or have slower than expected sales. If they neglect to inform the DC of that variance in demand, the DC continues to replenish the store based on the regular rate of demand, and the store ends up being over‐ or under‐supplied. Thus, DC fulfillment processes need to be intimately interconnected with store replenishment processes and needs. 8 For example, one type of produce might be higher priority because it is a key reason shoppers come to the store. Or a seasonal or holiday‐related item might take priority, like strawberries and blueberries that must arrive in time for Fourth of July sales.
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